OpenRaiser/Intern-Atlas huggingface dataset:
Unified Policy Optimization for Continuous-action Reinforcement Learning in Non-stationary Tasks and Games
Venue: arXiv cs.LG 2022
Year: 2022

This paper addresses policy learning in non-stationary environments and games with continuous actions. Rather than the classical reward maximization mechanism, inspired by the ideas of follow-the-regularized-leader (FTRL) and mirror descent (MD) update, we propose a no-regret style reinforcement learning algorithm PORL for continuous action tasks. We prove that PORL has a last-iterate convergence guarantee, which is important for adversarial and cooperative games. Empirical studies show that, in stationary environments such as MuJoCo locomotion controlling tasks, PORL performs equally well as, if not better than, the soft actor-critic (SAC) algorithm; in non-stationary environments including dynamical environments, adversarial training, and competitive games, PORL is superior to SAC in both a better final policy performance and a more stable training process.
Temporal View Synthesis of Dynamic Scenes through 3D Object Motion Estimation with Multi-Plane Images
Venue: arXiv cs.CV 2022
Year: 2022

The challenge of graphically rendering high frame-rate videos on low compute devices can be addressed through periodic prediction of future frames to enhance the user experience in virtual reality applications. This is studied through the problem of temporal view synthesis (TVS), where the goal is to predict the next frames of a video given the previous frames and the head poses of the previous and the next frames. In this work, we consider the TVS of dynamic scenes in which both the user and objects are moving. We design a framework that decouples the motion into user and object motion to effectively use the available user motion while predicting the next frames. We predict the motion of objects by isolating and estimating the 3D object motion in the past frames and then extrapolating it. We employ multi-plane images (MPI) as a 3D representation of the scenes and model the object motion as the 3D displacement between the corresponding points in the MPI representation. In order to handle the sparsity in MPIs while estimating the motion, we incorporate partial convolutions and masked correlation layers to estimate corresponding points. The predicted object motion is then integrated with the given user or camera motion to generate the next frame. Using a disocclusion infilling module, we synthesize the regions uncovered due to the camera and object motion. We develop a new synthetic dataset for TVS of dynamic scenes consisting of 800 videos at full HD resolution. We show through experiments on our dataset and the MPI Sintel dataset that our model outperforms all the competing methods in the literature.
Packet Forwarding with a Locally Bursty Adversary
Venue: arXiv cs.DC 2022
Year: 2022

We consider packet forwarding in the adversarial queueing theory (AQT) model introduced by Borodin et al. We introduce a refinement of the AQT $(ρ, σ)$-bounded adversary, which we call a \emph{locally bursty adversary} (LBA) that parameterizes injection patterns jointly by edge utilization and packet origin. For constant ($O(1)$) parameters, the LBA model is strictly more permissive than the $(ρ, σ)$ model. For example, there are injection patterns in the LBA model with constant parameters that can only be realized as $(ρ, σ)$-bounded injection patterns with $ρ+ σ= Ω(n)$ (where $n$ is the network size). We show that the LBA model (unlike the $(ρ, σ)$ model) is closed under packet bundling and discretization operations. Thus, the LBA model allows one to reduce the study of general (uniform) capacity networks and inhomogenous packet sizes to unit capacity networks with homogeneous packets. On the algorithmic side, we focus on information gathering networks -- i.e., networks in which all packets share a common destination, and the union of packet routes forms a tree. We show that the Odd-Even Downhill (OED) forwarding protocol described independently by Dobrev et al.\ and Patt-Shamir and Rosenbaum achieves buffer space usage of $O(\log n)$ against all LBAs with constant parameters. OED is a local protocol, but we show that the upper bound is tight even when compared to centralized protocols. Our lower bound for the LBA model is in contrast to the $(ρ, σ)$-model, where centralized protocols can achieve worst-case buffer space usage $O(1)$ for $ρ, σ= O(1)$, while the $O(\log n)$ upper bound for OED is optimal only for local protocols.
Generalized Projection Matrices
Venue: arXiv cs.GR 2022
Year: 2022

Projection matrices are necessary for a large portion of rendering computer graphics. There are primarily two different types of projection matrices -- perspective and orthographic -- which are used frequently, and are traditionally treated as mutually incompatible with each other in how they are defined. Here, we bridge the gap between the two different forms of projection matrices to present a single generalized projection matrix that can represent both.
Transforming the Interactive Segmentation for Medical Imaging
Venue: arXiv cs.CV 2022
Year: 2022

The goal of this paper is to interactively refine the automatic segmentation on challenging structures that fall behind human performance, either due to the scarcity of available annotations or the difficulty nature of the problem itself, for example, on segmenting cancer or small organs. Specifically, we propose a novel Transformer-based architecture for Interactive Segmentation (TIS), that treats the refinement task as a procedure for grouping pixels with similar features to those clicks given by the end users. Our proposed architecture is composed of Transformer Decoder variants, which naturally fulfills feature comparison with the attention mechanisms. In contrast to existing approaches, our proposed TIS is not limited to binary segmentations, and allows the user to edit masks for arbitrary number of categories. To validate the proposed approach, we conduct extensive experiments on three challenging datasets and demonstrate superior performance over the existing state-of-the-art methods. The project page is: https://wtliu7.github.io/tis/.
Vision-Language Matching for Text-to-Image Synthesis via Generative Adversarial Networks
Venue: arXiv cs.CV 2022
Year: 2022

Text-to-image synthesis aims to generate a photo-realistic and semantic consistent image from a specific text description. The images synthesized by off-the-shelf models usually contain limited components compared with the corresponding image and text description, which decreases the image quality and the textual-visual consistency. To address this issue, we propose a novel Vision-Language Matching strategy for text-to-image synthesis, named VLMGAN*, which introduces a dual vision-language matching mechanism to strengthen the image quality and semantic consistency. The dual vision-language matching mechanism considers textual-visual matching between the generated image and the corresponding text description, and visual-visual consistent constraints between the synthesized image and the real image. Given a specific text description, VLMGAN* firstly encodes it into textual features and then feeds them to a dual vision-language matching-based generative model to synthesize a photo-realistic and textual semantic consistent image. Besides, the popular evaluation metrics for text-to-image synthesis are borrowed from simple image generation, which mainly evaluates the reality and diversity of the synthesized images. Therefore, we introduce a metric named Vision-Language Matching Score (VLMS) to evaluate the performance of text-to-image synthesis which can consider both the image quality and the semantic consistency between synthesized image and the description. The proposed dual multi-level vision-language matching strategy can be applied to other text-to-image synthesis methods. We implement this strategy on two popular baselines, which are marked with ${\text{VLMGAN}_{+\text{AttnGAN}}}$ and ${\text{VLMGAN}_{+\text{DFGAN}}}$. The experimental results on two widely-used datasets show that the model achieves significant improvements over other state-of-the-art methods.
MemoNav: Selecting Informative Memories for Visual Navigation
Venue: arXiv cs.CV 2022
Year: 2022

Image-goal navigation is a challenging task, as it requires the agent to navigate to a target indicated by an image in a previously unseen scene. Current methods introduce diverse memory mechanisms which save navigation history to solve this task. However, these methods use all observations in the memory for generating navigation actions without considering which fraction of this memory is informative. To address this limitation, we present the MemoNav, a novel memory mechanism for image-goal navigation, which retains the agent's informative short-term memory and long-term memory to improve the navigation performance on a multi-goal task. The node features on the agent's topological map are stored in the short-term memory, as these features are dynamically updated. To aid the short-term memory, we also generate long-term memory by continuously aggregating the short-term memory via a graph attention module. The MemoNav retains the informative fraction of the short-term memory via a forgetting module based on a Transformer decoder and then incorporates this retained short-term memory and the long-term memory into working memory. Lastly, the agent uses the working memory for action generation. We evaluate our model on a new multi-goal navigation dataset. The experimental results show that the MemoNav outperforms the SoTA methods by a large margin with a smaller fraction of navigation history. The results also empirically show that our model is less likely to be trapped in a deadlock, which further validates that the MemoNav improves the agent's navigation efficiency by reducing redundant steps.
Learning to predict test effectiveness
Venue: arXiv cs.SE 2022
Year: 2022

The high cost of the test can be dramatically reduced, provided that the coverability as an inherent feature of the code under test is predictable. This article offers a machine learning model to predict the extent to which the test could cover a class in terms of a new metric called Coverageability. The prediction model consists of an ensemble of four regression models. The learning samples consist of feature vectors, where features are source code metrics computed for a class. The samples are labeled by the Coverageability values computed for their corresponding classes. We offer a mathematical model to evaluate test effectiveness in terms of size and coverage of the test suite generated automatically for each class. We extend the size of the feature space by introducing a new approach to defining sub-metrics in terms of existing source code metrics. Using feature importance analysis on the learned prediction models, we sort source code metrics in the order of their impact on the test effectiveness. As a result of which, we found the class strict cyclomatic complexity as the most influential source code metric. Our experiments with the prediction models on a large corpus of Java projects containing about 23,000 classes demonstrate the Mean Absolute Error (MAE) of 0.032, Mean Squared Error (MSE) of 0.004, and an R2-score of 0.855. Compared with the state-of-the-art coverage prediction models, our models improve MAE, MSE, and an R2-score by 5.78%, 2.84%, and 20.71%, respectively.
A biologically-inspired multi-modal evaluation of molecular generative machine learning
Venue: arXiv cs.LG 2022
Year: 2022

While generative models have recently become ubiquitous in many scientific areas, less attention has been paid to their evaluation. For molecular generative models, the state-of-the-art examines their output in isolation or in relation to its input. However, their biological and functional properties, such as ligand-target interaction is not being addressed. In this study, a novel biologically-inspired benchmark for the evaluation of molecular generative models is proposed. Specifically, three diverse reference datasets are designed and a set of metrics are introduced which are directly relevant to the drug discovery process. In particular we propose a recreation metric, apply drug-target affinity prediction and molecular docking as complementary techniques for the evaluation of generative outputs. While all three metrics show consistent results across the tested generative models, a more detailed comparison of drug-target affinity binding and molecular docking scores revealed that unimodal predictiors can lead to erroneous conclusions about target binding on a molecular level and a multi-modal approach is thus preferrable. The key advantage of this framework is that it incorporates prior physico-chemical domain knowledge into the benchmarking process by focusing explicitly on ligand-target interactions and thus creating a highly efficient tool not only for evaluating molecular generative outputs in particular, but also for enriching the drug discovery process in general.
Visual Analysis of Neural Architecture Spaces for Summarizing Design Principles
Venue: arXiv cs.HC 2022
Year: 2022

Recent advances in artificial intelligence largely benefit from better neural network architectures. These architectures are a product of a costly process of trial-and-error. To ease this process, we develop ArchExplorer, a visual analysis method for understanding a neural architecture space and summarizing design principles. The key idea behind our method is to make the architecture space explainable by exploiting structural distances between architectures. We formulate the pairwise distance calculation as solving an all-pairs shortest path problem. To improve efficiency, we decompose this problem into a set of single-source shortest path problems. The time complexity is reduced from O(kn^2N) to O(knN). Architectures are hierarchically clustered according to the distances between them. A circle-packing-based architecture visualization has been developed to convey both the global relationships between clusters and local neighborhoods of the architectures in each cluster. Two case studies and a post-analysis are presented to demonstrate the effectiveness of ArchExplorer in summarizing design principles and selecting better-performing architectures.
Lost in Context? On the Sense-wise Variance of Contextualized Word Embeddings
Venue: arXiv cs.CL 2022
Year: 2022

Contextualized word embeddings in language models have given much advance to NLP. Intuitively, sentential information is integrated into the representation of words, which can help model polysemy. However, context sensitivity also leads to the variance of representations, which may break the semantic consistency for synonyms. We quantify how much the contextualized embeddings of each word sense vary across contexts in typical pre-trained models. Results show that contextualized embeddings can be highly consistent across contexts. In addition, part-of-speech, number of word senses, and sentence length have an influence on the variance of sense representations. Interestingly, we find that word representations are position-biased, where the first words in different contexts tend to be more similar. We analyze such a phenomenon and also propose a simple way to alleviate such bias in distance-based word sense disambiguation settings.
Intelligent Omni-Surfaces: Simultaneous Refraction and Reflection for Full-dimensional Wireless Communications
Venue: arXiv cs.IT 2022
Year: 2022

The development of metasurfaces has unlocked various use cases in wireless communication networks to improve performance by manipulating the propagation environment. Intelligent omni-surface (IOS), an innovative technique in this category, is proposed for coverage extension. In contrast to the widely studied reflective metasurfaces, i.e., intelligent reflecting surfaces (IRSs), which can only serve receivers located on the same side of the transmitter, the IOS can achieve full-dimensional wireless communications by enabling the simultaneous reflection and refraction of the surface, and thus users on both sides can be served. In this paper, we provide a comprehensive overview of the state-of-the-art in IOS from the perspective of wireless communications, with the emphasis on their design principles, channel modeling, beamforming design, experimental implementation and measurements, as well as possible applications in future cellular networks. We first describe the basic concepts of metasurfaces, and introduce the corresponding design principles for different types of metasurfaces. Moreover, we elaborate on the reflective-refractive model for each IOS element and the channel model for IOS-aided wireless communication systems. Furthermore, we show how to achieve full-dimensional wireless communications with the IOS for three different scenarios. In particular, we present the implementation of an IOS-aided wireless communication prototype and report its experimental measurement results. Finally, we outline some potential future directions and challenges in this area.
Improving Multilayer-Perceptron(MLP)-based Network Anomaly Detection with Birch Clustering on CICIDS-2017 Dataset
Venue: arXiv cs.CR 2022
Year: 2022

Machine learning algorithms have been widely used in intrusion detection systems, including Multi-layer Perceptron (MLP). In this study, we proposed a two-stage model that combines the Birch clustering algorithm and MLP classifier to improve the performance of network anomaly multi-classification. In our proposed method, we first apply Birch or Kmeans as an unsupervised clustering algorithm to the CICIDS-2017 dataset to pre-group the data. The generated pseudo-label is then added as an additional feature to the training of the MLP-based classifier. The experimental results show that using Birch and K-Means clustering for data pre-grouping can improve intrusion detection system performance. Our method can achieve 99.73% accuracy in multi-classification using Birch clustering, which is better than similar researches using a stand-alone MLP model.
Sensor Security: Current Progress, Research Challenges, and Future Roadmap
Venue: arXiv cs.CR 2022
Year: 2022

Sensors are one of the most pervasive and integral components of today's safety-critical systems. Sensors serve as a bridge between physical quantities and connected systems. The connected systems with sensors blindly believe the sensor as there is no way to authenticate the signal coming from a sensor. This could be an entry point for an attacker. An attacker can inject a fake input signal along with the legitimate signal by using a suitable spoofing technique. As the sensor's transducer is not smart enough to differentiate between a fake and legitimate signal, the injected fake signal eventually can collapse the connected system. This type of attack is known as the transduction attack. Over the last decade, several works have been published to provide a defense against the transduction attack. However, the defenses are proposed on an ad-hoc basis; hence, they are not well-structured. Our work begins to fill this gap by providing a checklist that a defense technique should always follow to be considered as an ideal defense against the transduction attack. We name this checklist as the Golden reference of sensor defense. We provide insights on how this Golden reference can be achieved and argue that sensors should be redesigned from the transducer level to the sensor electronics level. We point out that only hardware or software modification is not enough; instead, a hardware/software (HW/SW) co-design approach is required to ride on this future roadmap to the robust and resilient sensor.
FLIS: Clustered Federated Learning via Inference Similarity for Non-IID Data Distribution
Venue: arXiv cs.LG 2022
Year: 2022

Classical federated learning approaches yield significant performance degradation in the presence of Non-IID data distributions of participants. When the distribution of each local dataset is highly different from the global one, the local objective of each client will be inconsistent with the global optima which incur a drift in the local updates. This phenomenon highly impacts the performance of clients. This is while the primary incentive for clients to participate in federated learning is to obtain better personalized models. To address the above-mentioned issue, we present a new algorithm, FLIS, which groups the clients population in clusters with jointly trainable data distributions by leveraging the inference similarity of clients' models. This framework captures settings where different groups of users have their own objectives (learning tasks) but by aggregating their data with others in the same cluster (same learning task) to perform more efficient and personalized federated learning. We present experimental results to demonstrate the benefits of FLIS over the state-of-the-art benchmarks on CIFAR-100/10, SVHN, and FMNIST datasets. Our code is available at https://github.com/MMorafah/FLIS.
ReckOn: A 28nm Sub-mm2 Task-Agnostic Spiking Recurrent Neural Network Processor Enabling On-Chip Learning over Second-Long Timescales
Venue: arXiv cs.NE 2022
Year: 2022

A robust real-world deployment of autonomous edge devices requires on-chip adaptation to user-, environment- and task-induced variability. Due to on-chip memory constraints, prior learning devices were limited to static stimuli with no temporal contents. We propose a 0.45-mm$^2$ spiking RNN processor enabling task-agnostic online learning over seconds, which we demonstrate for navigation, gesture recognition, and keyword spotting within a 0.8-% memory overhead and a <150-$μ$W training power budget.
The Development of a Labelled te reo Māori-English Bilingual Database for Language Technology
Venue: arXiv cs.CL 2022
Year: 2022

Te reo Māori (referred to as Māori), New Zealand's indigenous language, is under-resourced in language technology. Māori speakers are bilingual, where Māori is code-switched with English. Unfortunately, there are minimal resources available for Māori language technology, language detection and code-switch detection between Māori-English pair. Both English and Māori use Roman-derived orthography making rule-based systems for detecting language and code-switching restrictive. Most Māori language detection is done manually by language experts. This research builds a Māori-English bilingual database of 66,016,807 words with word-level language annotation. The New Zealand Parliament Hansard debates reports were used to build the database. The language labels are assigned using language-specific rules and expert manual annotations. Words with the same spelling, but different meanings, exist for Māori and English. These words could not be categorised as Māori or English based on word-level language rules. Hence, manual annotations were necessary. An analysis reporting the various aspects of the database such as metadata, year-wise analysis, frequently occurring words, sentence length and N-grams is also reported. The database developed here is a valuable tool for future language and speech technology development for Aotearoa New Zealand. The methodology followed to label the database can also be followed by other low-resourced language pairs.